Did Twitter’s Removal of Government and State-Affiliated Media Labels Expand the Influence of State Actors?


Allison Koh



Digital Democracy Workshop
University of Zurich
October 27, 2023

#TBT

“Unlike independent media, state-affiliated media frequently use their news coverage as a means to advance a political agenda. We believe that people have the right to know when a media account is affiliated directly or indirectly with a state actor” (Twitter, 2020).

Twitter: A safe space for state influence?


State Actors on Twitter

Russian interference in the US 2016 election
(Golovchenko et al. 2020; Ross, Vaccari, and Chadwick 2022)

Chinese influence operations and Xinjiang
(DiResta et al. 2021)

Iranian involvement in anti-Saudi discourse
(Kießling et al. 2020; Romm 2018)

Effect of Twitter’s changes

Tweet engagement in far-right networks ⬆️ since October 2022
(Barrie 2022)

Twitter’s Policy Changes in April 2023




March 29: ⬆️ engagement with prominent state media from China, Russia, Iran (Kann 2023)

April 6: “In the case of state-affiliated media entities, Twitter will not recommend or amplify accounts or their Tweets with these labels to people.”

April 12: NPR quits Twitter after being labeled as “state-affiliated media” (Folkenflik 2023)

April 21: Twitter removes all labels for “government” and “state-affiliated media”

Research Questions and Definitions

Research Questions

After Twitter’s removal of profile labels on April 21st, 2023…

  • …did engagement with state actors increase?
  • …did state actors change how they use the platform?


📖 Definitions1

  • State actor: Any individual or entity connected to government or state-affiliated media
  • Government official: Key individuals/entities representing “voices of the nation state abroad”
  • State-affiliated media: Outlets where the state exercises control over editorial content

Theoretical Expectations

Why should we expect changes on Twitter post-removal of state labels?



🧠 Key Concepts

  • Credibility1: Perceived trustworthiness and expertise of an information source
Type Attribute Signals
Perceived Source characteristics profile labels, disclosed affiliations, account type, verification badges
Persuasive Source behavior post frequency, topic diversity, other content-based features

Hypotheses and Exploratory Analysis

💡 Main Hypotheses

After Twitter officially removed labels from state actors’ accounts…

  • H1: …engagement with state actors increased.
    • Perceived credibility: RTs/likes
  • H2: …state actors changed their behavior on the platform.
    • Persuasive credibility: changes in post frequency/topic diversity
    • Assumption: Lower topic diversity = more directed, pro-regime messaging1

Hypotheses and Exploratory Analysis

🔎 Sub-hypotheses comparing state actor types

State actors with high credibility will be less responsive to platform changes.

  • 🏦 Official government accounts are credible voices of the state regardless of profile label.
  • 📺 State-affiliated media: more pronounced effects
    • Labels signal limited editorial independence
    • Label removal would boost perceived credibility
    • Opportunities to manipulate information/boost persuasive credibility

Hypotheses and Exploratory Analysis

🌏 Exploratory Analysis

  • Variation between state actors from different countries

Data

State Actors: Hamilton 2.0 Dashboard1


1,177 accounts linked to Chinese, Iranian, and Russian state actors

Proxy for accounts that had government and state-affiliated media labels

Data

Tweets: Twitter Academic API via twarc CLI





Tweet timeline endpoint; up to ~3,200 tweets per account

Collected ~2.5 million tweets from 1,038 timelines

Measurement

Dependent variables

Per-day metrics 📈

  • Retweets and likes of tweets authored by state actors (H1)
  • Tweet volumes of posts and Shannon Entropy of content produced by state actors (H2)


Independent variables

  • State actor type (official government or state-affiliated media)
  • Time-related variables for parametric tests

Results

Null results at the aggregate level

Placebo Tests—Engagement and Tweet Volume

 

 

 

 

Results

Disaggregated Analyses

Retweets





RTs of media accounts ⬆️


RTs of Russian accounts ⬆️

Results

Disaggregated Analyses

Likes





⬆️ likes of Russian accounts compared to Chinese/Iranian accounts

Results

Disaggregated Analyses

Tweets by state actors





⬆️ tweets by media accounts

⬆️ tweets by Russian and Chinese accounts

Conclusion

Results1

  • Compared to official government accounts, volumes of engagement and post frequency significantly increased for state-affiliated media accounts.
  • Per-country differences2

       Credibility       

China Iran Russia
Perceived ⬆️
Persuasive ⬆️ ⬆️


  • Next step: Measure per-day Shannon entropy, test hypotheses on topic diversity



Conclusion

Contributions

  • Descriptive, comparative overview of state actors’ online influence in the after times
  • By removing profile labels for state actors, Twitter has increased the space available for some of these digital authoritarians to exert influence on the platform.
  • In this research area, it is important to account for digital authoritarians’ social media activity across actor types and geopolitical contexts.
  • Broader implications for understanding how changes in platform infrastructure can expand the reach of contentious political actors

Thank you!

koh@hertie-school.org

https://allisonkoh.github.io/

🟦 @allisonwkoh

Appendix

Twitter: A safe space for state influence?

 

 

References

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Barrie, Christopher. 2022. “Did the Musk Takeover Boost Contentious Actors on Twitter?” arXiv Preprint arXiv:2212.10646. https://arxiv.org/abs/2212.10646.
DiResta, Renée, Josh A Goldstein, Carly Miller, and Harvey Wang. 2021. “One Topic, Two Networks: Evaluating Two Chinese Influence Operations on Twitter Related to Xinjiang.” Stanford Internet Observatory, December, 44.
Folkenflik, David. 2023. NPR Quits Twitter After Being Falsely Labeled as ’State-Affiliated Media’.” NPR, April.
Golovchenko, Yevgeniy, Cody Buntain, Gregory Eady, Megan A. Brown, and Joshua A. Tucker. 2020. “Cross-Platform State Propaganda: Russian Trolls on Twitter and YouTube During the 2016 U.S. Presidential Election.” The International Journal of Press/Politics 25 (3): 357–89. https://doi.org/10.1177/1940161220912682.
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Romm, Tony. 2018. “Iranians Masqueraded as Foreign Journalists to Push Political Messages Online, New Twitter Data Shows.” Washington Post, October.
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